Cajal-Retzius cells Trajectory

Load libraries

library(Seurat)
library(princurve)
library(monocle)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

#Set ggplot theme as classic
theme_set(theme_classic())

Load the full dataset

Hem.data <- readRDS("../QC.filtered.cells.RDS")
Idents(Hem.data) <- Hem.data$Cell_ident

DimPlot(object = Hem.data,
        group.by = "Cell_ident",
        reduction = "spring",
        cols = c("#83c3b8", #"ChP"
                 "#009fda", #"ChP_progenitors"
                 "#68b041", #"Dorso-Medial_pallium"
                 "#e46b6b", #"Hem"
                 "#e3c148", #"Medial_pallium"
                 "#b7d174", #2
                 "grey40", #4
                 "black", #5
                 "#3e69ac" #"Thalamic_eminence"
                 ))

Differentiating neurons trajectory

Neurons.data <-  subset(Hem.data, idents = c("seurat_clusters_2"))

DimPlot(Neurons.data ,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#b7d174")) + NoAxes()

## Split Pallial from Cajal-Retzius cells

p1 <- FeaturePlot(object = Neurons.data ,
            features = c("BP_signature1","LN_signature1"),
            pt.size = 0.5,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- FeaturePlot(object = Neurons.data ,
            features = c("Foxg1", "Trp73"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p1 / p2

Separation between the 2 lineage seems straightforward. We use louvain clustering to split the two.

Neurons.data <- RunPCA(Neurons.data, verbose = FALSE)

Neurons.data <- FindNeighbors(Neurons.data,
                              dims = 1:10,
                              k.param = 8)

Neurons.data <- FindClusters(Neurons.data, resolution = 0.05)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 2835
## Number of edges: 56608
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9640
## Number of communities: 2
## Elapsed time: 0 seconds

DimPlot(Neurons.data,
        reduction = "spring",
        cols = c("#cc391b","#026c9a"),
        pt.size = 0.5) & NoAxes()

Neurons.data$Lineage <- sapply(as.numeric(Neurons.data$SCT_snn_res.0.05),
                               FUN = function(x) {x= c("Hem","Pallial")[x]})

DimPlot(object = Neurons.data,
        group.by = "Lineage",
        reduction = "spring",
        cols = c("#cc391b","#026c9a"),
        pt.size = 0.5) & NoAxes()

## Fit principale curve on the two lineages

Cajal-Retzius cells

Trajectories.Hem <- Neurons.data@meta.data %>%
                    select("Barcodes", "nUMI", "Spring_1", "Spring_2", "Lineage") %>%
                    filter(Lineage == "Hem")

fit <- principal_curve(as.matrix(Trajectories.Hem[,c("Spring_1", "Spring_2")]),
                       smoother='lowess',
                       trace=TRUE,
                       f = .7,
                       stretch=0)

## Starting curve---distance^2: 45804778678
## Iteration 1---distance^2: 27732113
## Iteration 2---distance^2: 27728318

#The principal curve smoothed
Hem.pc.line <- as.data.frame(fit$s[order(fit$lambda),]) 

#Pseudotime score
Trajectories.Hem$PseudotimeScore <- fit$lambda/max(fit$lambda)

if (cor(Trajectories.Hem$PseudotimeScore, Neurons.data@assays$SCT@data['Hmga2', Trajectories.Hem$Barcodes]) > 0) {
  Trajectories.Hem$PseudotimeScore <- -(Trajectories.Hem$PseudotimeScore - max(Trajectories.Hem$PseudotimeScore))
}

Pallial neurons

Trajectories.Pallial <- Neurons.data@meta.data %>%
                        select("Barcodes", "nUMI", "Spring_1", "Spring_2", "Lineage") %>%
                        filter(Lineage == "Pallial")

fit <- principal_curve(as.matrix(Trajectories.Pallial[,c("Spring_1", "Spring_2")]),
                       smoother='lowess',
                       trace=TRUE,
                       f = .7,
                       stretch=0)

## Starting curve---distance^2: 26984853690
## Iteration 1---distance^2: 22153700
## Iteration 2---distance^2: 22179462
## Iteration 3---distance^2: 22180297

#The principal curve smoothed
Pallial.pc.line <- as.data.frame(fit$s[order(fit$lambda),])

#Pseudotime score
Trajectories.Pallial$PseudotimeScore <- fit$lambda/max(fit$lambda)

if (cor(Trajectories.Pallial$PseudotimeScore, Neurons.data@assays$SCT@data['Hmga2', Trajectories.Pallial$Barcodes]) > 0) {
  Trajectories.Pallial$PseudotimeScore <- -(Trajectories.Pallial$PseudotimeScore - max(Trajectories.Pallial$PseudotimeScore))
}

Combine the two trajectories’ data

Trajectories.neurons <- rbind(Trajectories.Pallial, Trajectories.Hem)

cols <- brewer.pal(n =11, name = "Spectral")

ggplot(Trajectories.neurons, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=PseudotimeScore), size=2, shape=16) + 
  scale_color_gradientn(colours=rev(cols), name='Speudotime score') +
  geom_line(data=Pallial.pc.line, color="#026c9a", size=0.77) +
  geom_line(data=Hem.pc.line, color="#cc391b", size=0.77)

## Plot pan-neuronal genes along this axis

Neurons.data <- NormalizeData(Neurons.data, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")

# Neurog2
p1 <- FeaturePlot(object = Neurons.data,
            features = c("Neurog2"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

Trajectories.neurons$Neurog2 <- Neurons.data@assays$RNA@data["Neurog2", Trajectories.neurons$Barcodes]

p2 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Neurog2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Tbr1 
p3 <- FeaturePlot(object = Neurons.data ,
            features = c("Tbr1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()
Trajectories.neurons$Tbr1 <- Neurons.data@assays$RNA@data["Tbr1", Trajectories.neurons$Barcodes]

p4 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Tbr1)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Mapt 
p5 <- FeaturePlot(object = Neurons.data ,
            features = c("Mapt"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

Trajectories.neurons$Mapt <- Neurons.data@assays$RNA@data["Mapt", Trajectories.neurons$Barcodes]

p6 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore, y= Mapt)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

p1 + p2 + p3 + p4 + p5 + p6 + patchwork::plot_layout(ncol = 2)

Shift Pseudotime in both lineage

Since we observe the first 25% of both trajectories are occupied by few, likely progenitor cells, we shift this cell along the axis

Pseudotime.intervals <- Trajectories.neurons%>%
                          select(Lineage, PseudotimeScore) %>%
                          mutate(Pseudotime.bins = cut(Trajectories.neurons$PseudotimeScore, seq(0, max(Trajectories.neurons$PseudotimeScore) + 0.05, 0.05), dig.lab = 2, right = FALSE)) %>%
                          group_by(Lineage, Pseudotime.bins) %>%
                          summarise(n=n())

ggplot(Pseudotime.intervals, aes(x=Pseudotime.bins, y=n, fill=Lineage)) +
        geom_bar(stat = "identity", width = 0.90) +
        theme(axis.text.x = element_text(angle = 45, hjust=1))+
        scale_fill_manual(values= c("#cc391b", "#026c9a"))

score <- sapply(Trajectories.neurons$PseudotimeScore,
                FUN = function(x) if (x <= 0.2) {x= 0.2} else { x=x })

Trajectories.neurons$PseudotimeScore.shifted <- (score - min(score)) / (max(score) - min(score))

# Neurog2
p1 <- FeaturePlot(object = Neurons.data ,
            features = c("Neurog2"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Neurog2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Tbr1 
p3 <- FeaturePlot(object = Neurons.data ,
            features = c("Tbr1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p4 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Tbr1)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

# Mapt 
p5 <- FeaturePlot(object = Neurons.data ,
            features = c("Mapt"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p6 <- ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= Mapt)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

p1 + p2 + p3 + p4 + p5 + p6 + patchwork::plot_layout(ncol = 2)

ggplot(Trajectories.neurons, aes(x= PseudotimeScore.shifted, y= nUMI/10000)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA)

rm(list = ls()[!ls() %in% c("Trajectories.neurons")])

Load progenitors with cell cycle trajectory fitted

Progenitors.data <- readRDS("../ProgenitorsDiversity/Progenitors.RDS")

table(Progenitors.data$Cell_ident)

## 
## Dorso-Medial_pallium                  Hem       Medial_pallium 
##                 3451                 1954                 2719

To balance the number of progenitors in both domain we will only work with Hem and Medial_pallium annotated cells. Since we are using pallial cell to contrast CR specific trajectory we think this approximation will not significantly affect our analysis.

Progenitors.data <-  subset(Progenitors.data, idents = c("Hem", "Medial_pallium"))

p1 <- DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols =  c("#e3c148", "#e46b6b")) + NoAxes()

p2 <- FeaturePlot(object = Progenitors.data,
            features = "Angle.cc",
            pt.size = 0.5,
            cols = rev(colorRampPalette(brewer.pal(n =10, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

p3 <- DimPlot(object = Progenitors.data,
        group.by = "Revelio.phase",
        pt.size = 0.5,
        reduction = "spring",
        cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 + p2 + p3  + patchwork::plot_layout(ncol = 2)

Combined progenitors and neurons along Pseudotime

# Start with neurons data
Trajectories.all <- Trajectories.neurons %>% select(Barcodes, nUMI, Spring_1, Spring_2, Lineage)

Trajectories.all$Pseudotime <- Trajectories.neurons$PseudotimeScore.shifted + 1
Trajectories.all$Phase <- NA

# Add progenitors data
Trajectories.progenitors <- Progenitors.data@meta.data %>%
                              select(Barcodes, nUMI, Spring_1, Spring_2) %>% 
                              mutate(Lineage= ifelse(Progenitors.data$Cell_ident == "Medial_pallium", "Pallial", "Hem") ,
                                     Pseudotime= Progenitors.data$Angle.cc,
                                     Phase = Progenitors.data$Revelio.phase)

Trajectories.all <- rbind(Trajectories.all, Trajectories.progenitors)

Trajectories.all$Phase <- factor(Trajectories.all$Phase, levels = c("G1.S", "S", "G2", "G2.M", "M.G1"))

p1 <- ggplot(Trajectories.all, aes(Spring_1, Spring_2)) +
        geom_point(aes(color=Pseudotime), size=0.5) + 
        scale_color_gradientn(colours=rev(brewer.pal(n =11, name = "Spectral")), name='Speudotime score')

p2 <- ggplot(Trajectories.all, aes(Spring_1, Spring_2)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a"))

p1 + p2

p1 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= nUMI/10000)) +
        geom_point(aes(color= Phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

p2 <- ggplot(Trajectories.all, aes(x= Pseudotime, y= nUMI/10000)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

p1 + p2

rm(list = ls()[!ls() %in% c("Trajectories.all")])

Subset the full dataset Seurat object

Hem.data <- readRDS("../QC.filtered.cells.RDS")

Neuro.trajectories <- CreateSeuratObject(counts = Hem.data@assays$RNA@data[, Trajectories.all$Barcodes],
                                         meta.data = Trajectories.all)

spring <- as.matrix(Neuro.trajectories@meta.data %>% select("Spring_1", "Spring_2"))
  
Neuro.trajectories[["spring"]] <- CreateDimReducObject(embeddings = spring, key = "Spring_", assay = DefaultAssay(Neuro.trajectories))

p1 <- FeaturePlot(object = Neuro.trajectories,
            features = "Pseudotime",
            pt.size = 1,
            cols = rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- DimPlot(object = Neuro.trajectories,
        group.by = "Lineage",
        pt.size = 1,
        reduction = "spring",
        cols =  c("#cc391b", "#026c9a")) & NoAxes()


p3 <- DimPlot(object = Neuro.trajectories,
        group.by = "Phase",
        pt.size = 1,
        reduction = "spring",
        cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 + p2 + p3

rm(list = ls()[!ls() %in% c("Neuro.trajectories")])

Normalization

Neuro.trajectories<- NormalizeData(Neuro.trajectories, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")

Neuro.trajectories <- FindVariableFeatures(Neuro.trajectories, selection.method = "vst", nfeatures = 2000, assay = "RNA")

Plot some genes along pseudotime

trend <- function(Seurat.data,
                  group.by,
                  gene){
  
  data <- Seurat.data@meta.data %>% select(Lineage, Pseudotime, Phase)
  data$Gene <- Seurat.data@assays$RNA@data[gene,]
  
  if (!group.by == "Lineage") {
    p <- ggplot(data=data, aes(x= Pseudotime, y= Gene)) +
        geom_point(aes(color= Phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA) +
        ggtitle(gene)
  } else {
    p <- ggplot(data=data, aes(x= Pseudotime, y= Gene)) +
        geom_point(aes(color= Lineage), size=0.5) +
        scale_color_manual(values= c("#cc391b", "#026c9a")) +
        geom_smooth(method="loess", n= 50, aes(color= Lineage)) +
        ylim(0,NA) +
        ggtitle(gene)
  }
  
  
  return(p)
}


Plot.Genes.trend <- function(Seurat.data,
                             group.by,
                             genes){
  pList <- mapply(FUN = trend, gene = genes,
                  MoreArgs = list(Seurat.data = Seurat.data, group.by=group.by),
                  SIMPLIFY = FALSE)
  print(x = cowplot::plot_grid(plotlist = pList, ncol = 2))
} 

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Gas1","Sox2",
                          "Neurog2", "Btg2",
                          "Tbr1", "Mapt",
                          "Trp73", "Foxg1"))

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Gmnc", "Mcidas",
                          "Foxj1", "Trp73",
                          "Lhx1", "Cdkn1a"))

Plot.Genes.trend(Seurat.data= Neuro.trajectories,
                 group.by = "Lineage",
                 genes= c("Mki67", "Top2a",
                          "H2afx", "Cdkn1c"))

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")

## [1] "06 décembre, 2021, 17,57"

#Packages used
sessionInfo()

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] splines   stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6   cowplot_1.1.1       ggExtra_0.9        
##  [4] RColorBrewer_1.1-2  dplyr_1.0.7         monocle_2.22.0     
##  [7] DDRTree_0.1.5       irlba_2.3.3         VGAM_1.1-5         
## [10] ggplot2_3.3.5       Biobase_2.54.0      BiocGenerics_0.40.0
## [13] Matrix_1.3-4        princurve_2.1.6     SeuratObject_4.0.4 
## [16] Seurat_4.0.5       
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15            colorspace_2.0-2      deldir_1.0-6         
##   [4] ellipsis_0.3.2        ggridges_0.5.3        spatstat.data_2.1-0  
##   [7] farver_2.1.0          leiden_0.3.9          listenv_0.8.0        
##  [10] ggrepel_0.9.1         fansi_0.5.0           codetools_0.2-18     
##  [13] docopt_0.7.1          knitr_1.36            polyclip_1.10-0      
##  [16] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.2        
##  [19] png_0.1-7             pheatmap_1.0.12       uwot_0.1.10          
##  [22] shiny_1.7.1           sctransform_0.3.2     spatstat.sparse_2.0-0
##  [25] compiler_4.1.2        httr_1.4.2            assertthat_0.2.1     
##  [28] fastmap_1.1.0         lazyeval_0.2.2        limma_3.50.0         
##  [31] later_1.3.0           htmltools_0.5.2       tools_4.1.2          
##  [34] igraph_1.2.9          gtable_0.3.0          glue_1.5.1           
##  [37] RANN_2.6.1            reshape2_1.4.4        Rcpp_1.0.7           
##  [40] slam_0.1-49           scattermore_0.7       jquerylib_0.1.4      
##  [43] vctrs_0.3.8           nlme_3.1-153          lmtest_0.9-39        
##  [46] xfun_0.28             stringr_1.4.0         globals_0.14.0       
##  [49] mime_0.12             miniUI_0.1.1.1        lifecycle_1.0.1      
##  [52] goftest_1.2-3         future_1.23.0         MASS_7.3-54          
##  [55] zoo_1.8-9             scales_1.1.1          spatstat.core_2.3-1  
##  [58] promises_1.2.0.1      spatstat.utils_2.2-0  parallel_4.1.2       
##  [61] yaml_2.2.1            reticulate_1.22       pbapply_1.5-0        
##  [64] gridExtra_2.3         sass_0.4.0            rpart_4.1-15         
##  [67] fastICA_1.2-3         stringi_1.7.6         highr_0.9            
##  [70] densityClust_0.3      rlang_0.4.12          pkgconfig_2.0.3      
##  [73] matrixStats_0.61.0    qlcMatrix_0.9.7       evaluate_0.14        
##  [76] lattice_0.20-45       ROCR_1.0-11           purrr_0.3.4          
##  [79] tensor_1.5            labeling_0.4.2        patchwork_1.1.1      
##  [82] htmlwidgets_1.5.4     tidyselect_1.1.1      parallelly_1.29.0    
##  [85] RcppAnnoy_0.0.19      plyr_1.8.6            magrittr_2.0.1       
##  [88] R6_2.5.1              generics_0.1.1        combinat_0.0-8       
##  [91] DBI_1.1.1             pillar_1.6.4          withr_2.4.3          
##  [94] mgcv_1.8-38           fitdistrplus_1.1-6    survival_3.2-13      
##  [97] abind_1.4-5           tibble_3.1.6          future.apply_1.8.1   
## [100] crayon_1.4.2          KernSmooth_2.23-20    utf8_1.2.2           
## [103] spatstat.geom_2.3-0   plotly_4.10.0         rmarkdown_2.11       
## [106] viridis_0.6.2         grid_4.1.2            data.table_1.14.2    
## [109] FNN_1.1.3             sparsesvd_0.2         HSMMSingleCell_1.14.0
## [112] digest_0.6.29         xtable_1.8-4          tidyr_1.1.4          
## [115] httpuv_1.6.3          munsell_0.5.0         viridisLite_0.4.0    
## [118] bslib_0.3.1

---

1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr↩︎